Mean Sea Surface Temperature

https://www.ncei.noaa.gov/products/optimum-interpolation-sst

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import warnings
warnings.filterwarnings("ignore")

import sys
import os
import os.path as op

import xarray as xr
import plotly.express as px
import geopandas as gpd
import pandas as pd
import cartopy.crs as ccrs
import numpy as np
import matplotlib.pyplot as plt

sys.path.append("../../../../indicators_setup")
from ind_setup.plotting import plot_base_map, plot_bar_probs, plot_map_subplots, add_oni_cat
from ind_setup.plotting_int import plot_timeseries_interactive, plot_oni_index_th

sys.path.append("../../../functions")
from data_downloaders import download_oni_index

lon_site, lat_site = 134.368203, 7.322074

#Area of interest
lon_range  = [129.4088, 137.0541]
lat_range = [1.5214, 11.6587]

shp_f = op.join(os.getcwd(), '..', '..','..', 'data/Palau_EEZ/pw_eez_pol_april2022.shp')
shp_eez = gpd.read_file(shp_f)

path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"
data = xr.load_dataset(op.join(path_data, 'sst_daily_1981_2024_palau.nc'))

dataset_id = 'sst'

ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
im = ax.pcolor(data.lon, data.lat, data.mean(dim='time')[dataset_id], transform=ccrs.PlateCarree(), 
                cmap = 'hot_r', vmin = np.percentile(data.mean(dim = 'time')[dataset_id], 1), 
                vmax = np.percentile(data.mean(dim = 'time')[dataset_id], 99))
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
plt.colorbar(im, ax=ax, label='SST (ºC)')

plt.savefig(op.join(path_figs, 'F12_SST_map.png'), dpi=300, bbox_inches='tight')

Annual average

data_y = data.resample(time='1YE').mean()
im = plot_map_subplots(data_y, dataset_id, shp_eez = shp_eez, cmap = 'hot_r', 
                  vmin = np.nanpercentile(data_y.min(dim = 'time')[dataset_id], 1), 
                  vmax = np.nanpercentile(data_y.max(dim = 'time')[dataset_id], 99),
                  cbar = 1, cbar_pad = 0.05)

Annual anomaly

data_an = data_y - data.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r',
                        vmin=-2, vmax=2, cbar = 1, cbar_pad = 0.05)

Average Area

data_mean = data.mean(dim = ['lon', 'lat']).to_dataframe()
datag = data_mean.groupby(data_mean.index.year).max()
datag.index = pd.to_datetime(datag.index, format = '%Y')
datag['sst_ref'] = datag['sst'] - datag.loc['1961':'1990'].sst.mean()

data_mean = data.mean(dim = ['lon', 'lat']).to_dataframe()
data_mean = data_mean.resample('YE').mean()
data_mean['sst_ref'] = data_mean['sst'] - data_mean.loc['1961':'1990'].sst.mean()
fig, ax, trend = plot_bar_probs(x=data_mean.index.year, y=data_mean.sst_ref, trendline=True,
                                y_label='SST [°C]', figsize=[15, 4], return_trend=True)
ax.set_title('Annual Mean SST anomalies (Over and above 1961 - 1990 reference period)', fontsize=15);
plt.savefig(op.join(path_figs, 'F12_SST_trends_Annomalies.png'), dpi=300, bbox_inches='tight')

Given point

loc = [7.37, 134.7]
dict_plot = [{'data' : data.sel(lon=loc[1], lat=loc[0], method='nearest').to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : 'SST [ºC]'},]

ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
ax.plot(loc[1], loc[0], '*', markersize = 12, color = 'royalblue', transform=ccrs.PlateCarree(), label = 'Location Analysis')
ax.legend()

<matplotlib.legend.Legend at 0x183943110>

fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12), label_yaxes = 'SST [ºC]');

ONI index analysis

p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
df1 = download_oni_index(p_data)

lims = [-.5, .5]
plot_oni_index_th(df1, lims = lims)

Group by ONI category

data_xr = xr.load_dataset(op.join(path_data, 'sst_daily_1981_2024_palau.nc')).rename({'lat':'latitude', 'lon':'longitude'}).resample(time='1M').mean()
data_xr['time'] = data_xr.time.values.astype('datetime64[M]')

data_xr['ONI'] = (('time'), df1.iloc[np.intersect1d(data_xr.time, df1.index, return_indices=True)[2]].ONI.values)
data_xr['ONI_cat'] = (('time'), np.where(data_xr.ONI < lims[0], -1, np.where(data_xr.ONI > lims[1], 1, 0)))
data_oni = data_xr.groupby('ONI_cat').mean()
data_oni['labels'] = (('ONI_cat'),['La Niña', 'Neutral', 'El Niño'])

dataset_id = 'sst'

fig = plot_map_subplots(data_oni, dataset_id, shp_eez = shp_eez, cmap = 'hot_r', 
                  vmin = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 0) - .25, 
                  vmax = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 100) + .25,
                  sub_plot= [1, 3], figsize = (15, 8), cbar = True, cbar_pad = .1)

plt.savefig(op.join(path_figs, 'F14_SST_ENSO.png'), dpi=300, bbox_inches='tight')

data_an = data_oni - data_xr.mean(dim='time')
data_an['labels'] = (('ONI_cat'),['La Niña', 'Neutral', 'El Niño'])
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.4, vmax=.4,
                  sub_plot= [1, 3], figsize = (15, 8), cbar = True, cbar_pad = 0.1)